{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License \n",
    "\n",
    "Copyright 2017 - 2020 Patrick Hall and the H2O.ai team\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DISCLAIMER:** This notebook is not legal compliance advice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing machine learning models for accuracy, trustworthiness, and stability with Python and H2O\n",
    "#### Performing residual analysis and sensitivity analysis to validate complex models\n",
    "\n",
    "This notebook provides a basic introduction to two traditional data analysis and model diagnostic techniques that can be applied to machine learning models: residual analysis and sensitivity analysis. The notebook starts by loading the UCI credit card default dataset and using h2o to train a GBM model to predict credit card defaults. Then, residual analysis is used to discover and debug an issue with the GBM, and the GBM is retrained and improved. The notebook concludes by conducting sensitivity analysis to test the GBM credit card default model for fairness and stability. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python imports\n",
    "In general, NumPy and Pandas will be used for data manipulation purposes and h2o will be used for modeling tasks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# h2o Python API with specific classes\n",
    "import h2o \n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
    "\n",
    "import numpy as np   # array, vector, matrix calculations\n",
    "import pandas as pd  # DataFrame handling\n",
    "\n",
    "pd.options.display.max_columns = 999 # enable display of all columns in notebook\n",
    "\n",
    "# plotting functionality\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# display plots in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start h2o\n",
    "H2o is both a library and a server. The machine learning algorithms in the library take advantage of the multithreaded and distributed architecture provided by the server to train machine learning algorithms extremely efficiently. The API for the library was imported above in cell 1, but the server still needs to be started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.8.0_201\"; Java(TM) SE Runtime Environment (build 1.8.0_201-b09); Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)\n",
      "  Starting server from /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpfq80fyby\n",
      "  JVM stdout: /tmp/tmpfq80fyby/h2o_patrickh_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpfq80fyby/h2o_patrickh_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.26.0.3</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>6 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_patrickh_ov75l0</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>1.778 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.26.0.3\n",
       "H2O cluster version age:    6 days\n",
       "H2O cluster name:           H2O_from_python_patrickh_ov75l0\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.778 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.4 final\n",
       "--------------------------  ---------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init(max_mem_size='2G')       # start h2o\n",
    "h2o.remove_all()                  # remove any existing data structures from h2o memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download, explore, and prepare UCI credit card default data\n",
    "\n",
    "UCI credit card default data: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n",
    "\n",
    "The UCI credit card default data contains demographic and payment information about credit card customers in Taiwan in the year 2005. The data set contains 23 input variables: \n",
    "\n",
    "* **`LIMIT_BAL`**: Amount of given credit (NT dollar)\n",
    "* **`SEX`**: 1 = male; 2 = female\n",
    "* **`EDUCATION`**: 1 = graduate school; 2 = university; 3 = high school; 4 = others \n",
    "* **`MARRIAGE`**: 1 = married; 2 = single; 3 = others\n",
    "* **`AGE`**: Age in years \n",
    "* **`PAY_0`, `PAY_2` - `PAY_6`**: History of past payment; `PAY_0` = the repayment status in September, 2005; `PAY_2` = the repayment status in August, 2005; ...; `PAY_6` = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n",
    "* **`BILL_AMT1` - `BILL_AMT6`**: Amount of bill statement (NT dollar). `BILL_AMNT1` = amount of bill statement in September, 2005; `BILL_AMT2` = amount of bill statement in August, 2005; ...; `BILL_AMT6` = amount of bill statement in April, 2005. \n",
    "* **`PAY_AMT1` - `PAY_AMT6`**: Amount of previous payment (NT dollar). `PAY_AMT1` = amount paid in September, 2005; `PAY_AMT2` = amount paid in August, 2005; ...; `PAY_AMT6` = amount paid in April, 2005. \n",
    "\n",
    "These 23 input variables are used to predict the target variable, whether or not a customer defaulted on their credit card bill in late 2005.\n",
    "\n",
    "Because h2o accepts both numeric and character inputs, some variables will be recoded into more transparent character values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data and clean\n",
    "The credit card default data is available as an `.xls` file. Pandas reads `.xls` files automatically, so it's used to load the credit card default data and give the prediction target a shorter name: `DEFAULT_NEXT_MONTH`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import XLS file\n",
    "path = 'default_of_credit_card_clients.xls'\n",
    "data = pd.read_excel(path,\n",
    "                     skiprows=1)\n",
    "\n",
    "# remove spaces from target column name \n",
    "data = data.rename(columns={'default payment next month': 'DEFAULT_NEXT_MONTH'}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign modeling roles\n",
    "The shorthand name `y` is assigned to the prediction target. `X` is assigned to all other input variables in the credit card default data except the row indentifier, `ID`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = DEFAULT_NEXT_MONTH\n",
      "X = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n"
     ]
    }
   ],
   "source": [
    "# assign target and inputs for GBM\n",
    "y = 'DEFAULT_NEXT_MONTH'\n",
    "X = [name for name in data.columns if name not in [y, 'ID']]\n",
    "print('y =', y)\n",
    "print('X =', X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper function for recoding values in the UCI credict card default data\n",
    "This simple function maps longer, more understandable character string values from the UCI credit card default data dictionary to the original integer values of the input variables found in the dataset. These character values can be used directly in h2o decision tree models, and the function returns the original Pandas DataFrame as an h2o object, an H2OFrame. H2o models cannot run on Pandas DataFrames. They require H2OFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "def recode_cc_data(frame):\n",
    "    \n",
    "    \"\"\" Recodes numeric categorical variables into categorical character variables\n",
    "    with more transparent values. \n",
    "    \n",
    "    Args:\n",
    "        frame: Pandas DataFrame version of UCI credit card default data.\n",
    "        \n",
    "    Returns: \n",
    "        H2OFrame with recoded values.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    # define recoded values\n",
    "    sex_dict = {1:'male', 2:'female'}\n",
    "    education_dict = {0:'other', 1:'graduate school', 2:'university', 3:'high school', \n",
    "                      4:'other', 5:'other', 6:'other'}\n",
    "    marriage_dict = {0:'other', 1:'married', 2:'single', 3:'divorced'}\n",
    "    pay_dict = {-2:'no consumption', -1:'pay duly', 0:'use of revolving credit', 1:'1 month delay', \n",
    "                2:'2 month delay', 3:'3 month delay', 4:'4 month delay', 5:'5 month delay', 6:'6 month delay', \n",
    "                7:'7 month delay', 8:'8 month delay', 9:'9+ month delay'}\n",
    "    \n",
    "    # recode values using Pandas apply() and anonymous function\n",
    "    frame['SEX'] = frame['SEX'].apply(lambda i: sex_dict[i])\n",
    "    frame['EDUCATION'] = frame['EDUCATION'].apply(lambda i: education_dict[i])    \n",
    "    frame['MARRIAGE'] = frame['MARRIAGE'].apply(lambda i: marriage_dict[i]) \n",
    "    for name in frame.columns:\n",
    "        if name in ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']:\n",
    "            frame[name] = frame[name].apply(lambda i: pay_dict[i])            \n",
    "                \n",
    "    return h2o.H2OFrame(frame)\n",
    "\n",
    "data = recode_cc_data(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ensure target is handled as a categorical variable\n",
    "In h2o, a numeric variable can be treated as numeric or categorical. The target variable `DEFAULT_NEXT_MONTH` takes on values of `0` or `1`. To ensure this numeric variable is treated as a categorical variable, the `asfactor()` function is used to explicitly declare that it is a categorical variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[y] = data[y].asfactor() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display descriptive statistics\n",
    "The h2o `describe()` function displays a brief description of the credit card default data. For the categorical input variables `LIMIT_BAL`, `SEX`, `EDUCATION`, `MARRIAGE`, and `PAY_0`-`PAY_6`, the new character values created above in cell 5 are visible. Basic descriptive statistics are displayed for numeric inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:30000\n",
      "Cols:25\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>ID               </th><th>LIMIT_BAL         </th><th>SEX   </th><th>EDUCATION      </th><th>MARRIAGE  </th><th>AGE              </th><th>PAY_0                  </th><th>PAY_2                  </th><th>PAY_3                  </th><th>PAY_4                  </th><th>PAY_5                  </th><th>PAY_6                  </th><th>BILL_AMT1        </th><th>BILL_AMT2        </th><th>BILL_AMT3        </th><th>BILL_AMT4        </th><th>BILL_AMT5        </th><th>BILL_AMT6        </th><th>PAY_AMT1          </th><th>PAY_AMT2          </th><th>PAY_AMT3          </th><th>PAY_AMT4          </th><th>PAY_AMT5          </th><th>PAY_AMT6          </th><th>DEFAULT_NEXT_MONTH  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int              </td><td>int               </td><td>enum  </td><td>enum           </td><td>enum      </td><td>int              </td><td>enum                   </td><td>enum                   </td><td>enum                   </td><td>enum                   </td><td>enum                   </td><td>enum                   </td><td>int              </td><td>int              </td><td>int              </td><td>int              </td><td>int              </td><td>int              </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>enum                </td></tr>\n",
       "<tr><td>mins   </td><td>1.0              </td><td>10000.0           </td><td>      </td><td>               </td><td>          </td><td>21.0             </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>-165580.0        </td><td>-69777.0         </td><td>-157264.0        </td><td>-170000.0        </td><td>-81334.0         </td><td>-339603.0        </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>                    </td></tr>\n",
       "<tr><td>mean   </td><td>15000.5          </td><td>167484.32266666688</td><td>      </td><td>               </td><td>          </td><td>35.48549999999994</td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>51223.33090000009</td><td>49179.07516666668</td><td>47013.15479999971</td><td>43262.9489666666 </td><td>40311.40096666653</td><td>38871.76039999991</td><td>5663.580500000014 </td><td>5921.16350000001  </td><td>5225.681500000005 </td><td>4826.076866666661 </td><td>4799.387633333302 </td><td>5215.502566666664 </td><td>                    </td></tr>\n",
       "<tr><td>maxs   </td><td>30000.0          </td><td>1000000.0         </td><td>      </td><td>               </td><td>          </td><td>79.0             </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>964511.0         </td><td>983931.0         </td><td>1664089.0        </td><td>891586.0         </td><td>927171.0         </td><td>961664.0         </td><td>873552.0          </td><td>1684259.0         </td><td>896040.0          </td><td>621000.0          </td><td>426529.0          </td><td>528666.0          </td><td>                    </td></tr>\n",
       "<tr><td>sigma  </td><td>8660.398374208891</td><td>129747.66156720225</td><td>      </td><td>               </td><td>          </td><td>9.21790406809016 </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>73635.86057552959</td><td>71173.76878252836</td><td>69349.38742703681</td><td>64332.85613391641</td><td>60797.1557702648 </td><td>59554.10753674574</td><td>16563.280354025763</td><td>23040.870402057226</td><td>17606.961469803115</td><td>15666.159744031993</td><td>15278.305679144793</td><td>17777.465775435332</td><td>                    </td></tr>\n",
       "<tr><td>zeros  </td><td>0                </td><td>0                 </td><td>      </td><td>               </td><td>          </td><td>0                </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>                       </td><td>2008             </td><td>2506             </td><td>2870             </td><td>3195             </td><td>3506             </td><td>4020             </td><td>5249              </td><td>5396              </td><td>5968              </td><td>6408              </td><td>6703              </td><td>7173              </td><td>                    </td></tr>\n",
       "<tr><td>missing</td><td>0                </td><td>0                 </td><td>0     </td><td>0              </td><td>0         </td><td>0                </td><td>0                      </td><td>0                      </td><td>0                      </td><td>0                      </td><td>0                      </td><td>0                      </td><td>0                </td><td>0                </td><td>0                </td><td>0                </td><td>0                </td><td>0                </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                   </td></tr>\n",
       "<tr><td>0      </td><td>1.0              </td><td>20000.0           </td><td>female</td><td>university     </td><td>married   </td><td>24.0             </td><td>2 month delay          </td><td>2 month delay          </td><td>pay duly               </td><td>pay duly               </td><td>no consumption         </td><td>no consumption         </td><td>3913.0           </td><td>3102.0           </td><td>689.0            </td><td>0.0              </td><td>0.0              </td><td>0.0              </td><td>0.0               </td><td>689.0             </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>1                   </td></tr>\n",
       "<tr><td>1      </td><td>2.0              </td><td>120000.0          </td><td>female</td><td>university     </td><td>single    </td><td>26.0             </td><td>pay duly               </td><td>2 month delay          </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>2 month delay          </td><td>2682.0           </td><td>1725.0           </td><td>2682.0           </td><td>3272.0           </td><td>3455.0           </td><td>3261.0           </td><td>0.0               </td><td>1000.0            </td><td>1000.0            </td><td>1000.0            </td><td>0.0               </td><td>2000.0            </td><td>1                   </td></tr>\n",
       "<tr><td>2      </td><td>3.0              </td><td>90000.0           </td><td>female</td><td>university     </td><td>single    </td><td>34.0             </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>29239.0          </td><td>14027.0          </td><td>13559.0          </td><td>14331.0          </td><td>14948.0          </td><td>15549.0          </td><td>1518.0            </td><td>1500.0            </td><td>1000.0            </td><td>1000.0            </td><td>1000.0            </td><td>5000.0            </td><td>0                   </td></tr>\n",
       "<tr><td>3      </td><td>4.0              </td><td>50000.0           </td><td>female</td><td>university     </td><td>married   </td><td>37.0             </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>46990.0          </td><td>48233.0          </td><td>49291.0          </td><td>28314.0          </td><td>28959.0          </td><td>29547.0          </td><td>2000.0            </td><td>2019.0            </td><td>1200.0            </td><td>1100.0            </td><td>1069.0            </td><td>1000.0            </td><td>0                   </td></tr>\n",
       "<tr><td>4      </td><td>5.0              </td><td>50000.0           </td><td>male  </td><td>university     </td><td>married   </td><td>57.0             </td><td>pay duly               </td><td>use of revolving credit</td><td>pay duly               </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>8617.0           </td><td>5670.0           </td><td>35835.0          </td><td>20940.0          </td><td>19146.0          </td><td>19131.0          </td><td>2000.0            </td><td>36681.0           </td><td>10000.0           </td><td>9000.0            </td><td>689.0             </td><td>679.0             </td><td>0                   </td></tr>\n",
       "<tr><td>5      </td><td>6.0              </td><td>50000.0           </td><td>male  </td><td>graduate school</td><td>single    </td><td>37.0             </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>64400.0          </td><td>57069.0          </td><td>57608.0          </td><td>19394.0          </td><td>19619.0          </td><td>20024.0          </td><td>2500.0            </td><td>1815.0            </td><td>657.0             </td><td>1000.0            </td><td>1000.0            </td><td>800.0             </td><td>0                   </td></tr>\n",
       "<tr><td>6      </td><td>7.0              </td><td>500000.0          </td><td>male  </td><td>graduate school</td><td>single    </td><td>29.0             </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>367965.0         </td><td>412023.0         </td><td>445007.0         </td><td>542653.0         </td><td>483003.0         </td><td>473944.0         </td><td>55000.0           </td><td>40000.0           </td><td>38000.0           </td><td>20239.0           </td><td>13750.0           </td><td>13770.0           </td><td>0                   </td></tr>\n",
       "<tr><td>7      </td><td>8.0              </td><td>100000.0          </td><td>female</td><td>university     </td><td>single    </td><td>23.0             </td><td>use of revolving credit</td><td>pay duly               </td><td>pay duly               </td><td>use of revolving credit</td><td>use of revolving credit</td><td>pay duly               </td><td>11876.0          </td><td>380.0            </td><td>601.0            </td><td>221.0            </td><td>-159.0           </td><td>567.0            </td><td>380.0             </td><td>601.0             </td><td>0.0               </td><td>581.0             </td><td>1687.0            </td><td>1542.0            </td><td>0                   </td></tr>\n",
       "<tr><td>8      </td><td>9.0              </td><td>140000.0          </td><td>female</td><td>high school    </td><td>married   </td><td>28.0             </td><td>use of revolving credit</td><td>use of revolving credit</td><td>2 month delay          </td><td>use of revolving credit</td><td>use of revolving credit</td><td>use of revolving credit</td><td>11285.0          </td><td>14096.0          </td><td>12108.0          </td><td>12211.0          </td><td>11793.0          </td><td>3719.0           </td><td>3329.0            </td><td>0.0               </td><td>432.0             </td><td>1000.0            </td><td>1000.0            </td><td>1000.0            </td><td>0                   </td></tr>\n",
       "<tr><td>9      </td><td>10.0             </td><td>20000.0           </td><td>male  </td><td>high school    </td><td>single    </td><td>35.0             </td><td>no consumption         </td><td>no consumption         </td><td>no consumption         </td><td>no consumption         </td><td>pay duly               </td><td>pay duly               </td><td>0.0              </td><td>0.0              </td><td>0.0              </td><td>0.0              </td><td>13007.0          </td><td>13912.0          </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>13007.0           </td><td>1122.0            </td><td>0.0               </td><td>0                   </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Train an H2O GBM classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into training and test sets for early stopping\n",
    "The credit card default data is split into training and test sets to monitor and prevent overtraining. Reproducibility is also important factor in creating trustworthy models, and randomly splitting datasets can introduce randomness in model predictions and other results. A random seed is used here to ensure the data split is reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data rows = 21060, columns = 25\n",
      "Test data rows = 8940, columns = 25\n"
     ]
    }
   ],
   "source": [
    "# split into training and validation\n",
    "train, test = data.split_frame([0.7], seed=12345)\n",
    "\n",
    "# summarize split\n",
    "print('Train data rows = %d, columns = %d' % (train.shape[0], train.shape[1]))\n",
    "print('Test data rows = %d, columns = %d' % (test.shape[0], test.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train h2o GBM classifier\n",
    "Many tuning parameters must be specified to train a GBM using h2o. Typically a grid search would be performed to identify the best parameters for a given modeling task using the `H2OGridSearch` class. For brevity's sake, a previously-discovered set of good tuning parameters are specified here. Because gradient boosting methods typically resample training data, an additional random seed is also specified for the h2o GBM using the `seed` parameter to create reproducible predictions, error rates, and variable importance values. To avoid overfitting, the `stopping_rounds` parameter is used to stop the training process after the test error fails to decrease for 5 iterations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "GBM Test AUC = 0.7804\n"
     ]
    }
   ],
   "source": [
    "# initialize GBM model\n",
    "model = H2OGradientBoostingEstimator(ntrees=150,            # maximum 150 trees in GBM\n",
    "                                     max_depth=4,           # trees can have maximum depth of 4\n",
    "                                     sample_rate=0.9,       # use 90% of rows in each iteration (tree)\n",
    "                                     col_sample_rate=0.9,   # use 90% of variables in each iteration (tree)\n",
    "                                     stopping_rounds=5,     # stop if validation error does not decrease for 5 iterations (trees)\n",
    "                                     seed=12345)            # for reproducibility\n",
    "\n",
    "# train a GBM model\n",
    "model.train(y=y, x=X, training_frame=train, validation_frame=test)\n",
    "\n",
    "# print AUC\n",
    "print('GBM Test AUC = %.4f' % model.auc(valid=True))\n",
    "\n",
    "# uncomment to see model details\n",
    "# print(model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display variable importance\n",
    "During training, the h2o GBM aggregates the improvement in error caused by each split in each decision tree across all the decision trees in the ensemble classifier. These values are attributed to the input variable used in each split and give an indication of the contribution each input variable makes toward the model's predictions. The variable importance ranking should be parsimonious with human domain knowledge and reasonable expectations. In this case, a customer's most recent payment behavior, `PAY_0`, is by far the most important variable followed by their second most recent payment, `PAY_2`, their credit limit, `LIMIT_BAL`, and third most recent payment behavior, `PAY_3`. This result is well-aligned with business practices in credit lending: people who miss their most recent payments are likely to default soon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7350aa39b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.varimp_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Conduct residual analysis to debug model\n",
    "Residuals refer to the difference between the recorded value of a dependent variable and the predicted value of a dependent variable for every row in a data set. Plotting the residual values against the predicted values is a time-honored model assessment technique and a great way to see all your modeling results in two dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bind model predictions onto test data \n",
    "To calculate the residuals for our GBM model, first the model predictions are merged onto onto the test set. The test data is used here to see how the model behaves on holdout data, which should be closer to its behavior on new data than analyzing residuals for the training inputs and predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "yhat = 'p_DEFAULT_NEXT_MONTH'\n",
    "preds1 = model.predict(test).drop(['predict', 'p0'])\n",
    "preds1.columns = [yhat]\n",
    "test_yhat = test.cbind(preds1[yhat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate deviance residuals for binomial classification\n",
    "For binomial classification, deviance residuals are related to the logloss cost function. Like analyzing $y - \\hat{y}$ for linear regression, these residuals are the quantities that the GBM sought to minimize. Deviance residual values are calculated by applying the simple formula in the cell directly below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use Pandas for adding columns and plotting\n",
    "test_yhat = test_yhat.as_data_frame()\n",
    "test_yhat['s'] = 1\n",
    "test_yhat.loc[test_yhat['DEFAULT_NEXT_MONTH'] == 0, 's'] = -1\n",
    "test_yhat['r_DEFAULT_NEXT_MONTH'] = test_yhat['s'] * np.sqrt(-2*(test_yhat[y]*np.log(test_yhat[yhat]) +\n",
    "                                                                 ((1 - test_yhat[y])*np.log(1 - test_yhat[yhat]))))\n",
    "test_yhat = test_yhat.drop('s', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot residuals\n",
    "Plotting residuals is a model debugging and diagnostic tool that enables users to see modeling results, and any anomolies, in a single two-dimensional plot. Here the green points represent customers who defaulted, and the blue points represent customers who did not. A few potential outliers are visible. There appear to be several cases in the test data with relatively large negative residuals. Understanding and addressing the factors that cause these outliers could lead to a more acccurate model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7374c7a400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = test_yhat.groupby('DEFAULT_NEXT_MONTH') # define groups\n",
    "fig, ax_ = plt.subplots(figsize=(8, 8))          # initialize figure\n",
    "\n",
    "plt.xlabel('Predicted: DEFAULT_NEXT_MONTH')\n",
    "plt.ylabel('Residual: DEFAULT_NEXT_MONTH')\n",
    "\n",
    "# plot groups with appropriate color\n",
    "color_list = ['b', 'g'] \n",
    "c_idx = 0\n",
    "for name, group in groups:\n",
    "    ax_.plot(group.p_DEFAULT_NEXT_MONTH, group.r_DEFAULT_NEXT_MONTH, label=' '.join(['DEFAULT_NEXT_MONTH:', str(name)]),\n",
    "             marker='o', linestyle='', color=color_list[c_idx], alpha=0.3)\n",
    "    c_idx += 1\n",
    "\n",
    "_ = ax_.legend(loc=1) # legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sort data by residuals and display data and residuals\n",
    "Printing a table with model inputs, actual target values, and model predictions sorted by residuals is another simple way to analyze residuals. Customers that defaulted, but were predicted not to, are listed at the top of the table below. Scroll to the bottom of the table to see the customers who were predicted to default, but then did not. Also notice the jumps in residual values. These are the potential outliers pictured in the residual plot above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEFAULT_NEXT_MONTH</th>\n",
       "      <th>p_DEFAULT_NEXT_MONTH</th>\n",
       "      <th>r_DEFAULT_NEXT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2561</td>\n",
       "      <td>310000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>32</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>20138</td>\n",
       "      <td>8267</td>\n",
       "      <td>65993</td>\n",
       "      <td>8543</td>\n",
       "      <td>1695</td>\n",
       "      <td>750</td>\n",
       "      <td>8267</td>\n",
       "      <td>66008</td>\n",
       "      <td>8543</td>\n",
       "      <td>1695</td>\n",
       "      <td>750</td>\n",
       "      <td>7350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.045837</td>\n",
       "      <td>2.483007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3016</td>\n",
       "      <td>350000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>38</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>16459</td>\n",
       "      <td>4120</td>\n",
       "      <td>44164</td>\n",
       "      <td>35233</td>\n",
       "      <td>884</td>\n",
       "      <td>9924</td>\n",
       "      <td>941</td>\n",
       "      <td>44743</td>\n",
       "      <td>0</td>\n",
       "      <td>884</td>\n",
       "      <td>9924</td>\n",
       "      <td>10824</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050230</td>\n",
       "      <td>2.445869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11462</td>\n",
       "      <td>210000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>46</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>15655</td>\n",
       "      <td>3918</td>\n",
       "      <td>29881</td>\n",
       "      <td>24247</td>\n",
       "      <td>21664</td>\n",
       "      <td>1556</td>\n",
       "      <td>4854</td>\n",
       "      <td>30366</td>\n",
       "      <td>0</td>\n",
       "      <td>433</td>\n",
       "      <td>1556</td>\n",
       "      <td>14047</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050527</td>\n",
       "      <td>2.443456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25772</td>\n",
       "      <td>350000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>33</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>82964</td>\n",
       "      <td>68532</td>\n",
       "      <td>17926</td>\n",
       "      <td>17966</td>\n",
       "      <td>30741</td>\n",
       "      <td>31088</td>\n",
       "      <td>68940</td>\n",
       "      <td>18018</td>\n",
       "      <td>18058</td>\n",
       "      <td>30897</td>\n",
       "      <td>31244</td>\n",
       "      <td>88461</td>\n",
       "      <td>1</td>\n",
       "      <td>0.051503</td>\n",
       "      <td>2.435615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6933</td>\n",
       "      <td>500000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>37</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>4331</td>\n",
       "      <td>60446</td>\n",
       "      <td>30592</td>\n",
       "      <td>154167</td>\n",
       "      <td>13410</td>\n",
       "      <td>25426</td>\n",
       "      <td>60446</td>\n",
       "      <td>30594</td>\n",
       "      <td>150843</td>\n",
       "      <td>163881</td>\n",
       "      <td>25426</td>\n",
       "      <td>39526</td>\n",
       "      <td>1</td>\n",
       "      <td>0.051717</td>\n",
       "      <td>2.433910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22505</td>\n",
       "      <td>260000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>33</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>5188</td>\n",
       "      <td>12357</td>\n",
       "      <td>28656</td>\n",
       "      <td>7497</td>\n",
       "      <td>7685</td>\n",
       "      <td>15434</td>\n",
       "      <td>13000</td>\n",
       "      <td>29022</td>\n",
       "      <td>7500</td>\n",
       "      <td>27769</td>\n",
       "      <td>12000</td>\n",
       "      <td>6200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053061</td>\n",
       "      <td>2.423347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22751</td>\n",
       "      <td>350000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>32</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>30625</td>\n",
       "      <td>60003</td>\n",
       "      <td>7147</td>\n",
       "      <td>9950</td>\n",
       "      <td>22117</td>\n",
       "      <td>4874</td>\n",
       "      <td>60396</td>\n",
       "      <td>7147</td>\n",
       "      <td>9950</td>\n",
       "      <td>22117</td>\n",
       "      <td>4874</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056650</td>\n",
       "      <td>2.396193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13381</td>\n",
       "      <td>400000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>35</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>109943</td>\n",
       "      <td>222085</td>\n",
       "      <td>223350</td>\n",
       "      <td>213831</td>\n",
       "      <td>210563</td>\n",
       "      <td>211925</td>\n",
       "      <td>120018</td>\n",
       "      <td>10071</td>\n",
       "      <td>8037</td>\n",
       "      <td>8018</td>\n",
       "      <td>8809</td>\n",
       "      <td>5022</td>\n",
       "      <td>1</td>\n",
       "      <td>0.058753</td>\n",
       "      <td>2.380929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19530</td>\n",
       "      <td>350000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>36</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>21026</td>\n",
       "      <td>35588</td>\n",
       "      <td>38002</td>\n",
       "      <td>40357</td>\n",
       "      <td>43663</td>\n",
       "      <td>52735</td>\n",
       "      <td>15000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>10000</td>\n",
       "      <td>25000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.058911</td>\n",
       "      <td>2.379801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15549</td>\n",
       "      <td>450000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>36</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>8012</td>\n",
       "      <td>4009</td>\n",
       "      <td>5226</td>\n",
       "      <td>4715</td>\n",
       "      <td>3275</td>\n",
       "      <td>6422</td>\n",
       "      <td>4021</td>\n",
       "      <td>5241</td>\n",
       "      <td>4729</td>\n",
       "      <td>3284</td>\n",
       "      <td>6441</td>\n",
       "      <td>4285</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059060</td>\n",
       "      <td>2.378739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25692</td>\n",
       "      <td>330000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>42</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>565</td>\n",
       "      <td>20650</td>\n",
       "      <td>15360</td>\n",
       "      <td>0</td>\n",
       "      <td>12923</td>\n",
       "      <td>1816</td>\n",
       "      <td>20650</td>\n",
       "      <td>15360</td>\n",
       "      <td>0</td>\n",
       "      <td>12923</td>\n",
       "      <td>1816</td>\n",
       "      <td>17050</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059089</td>\n",
       "      <td>2.378531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>971</td>\n",
       "      <td>300000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>42</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>11973</td>\n",
       "      <td>61834</td>\n",
       "      <td>25145</td>\n",
       "      <td>37666</td>\n",
       "      <td>19453</td>\n",
       "      <td>10492</td>\n",
       "      <td>20979</td>\n",
       "      <td>5000</td>\n",
       "      <td>37676</td>\n",
       "      <td>8808</td>\n",
       "      <td>2000</td>\n",
       "      <td>2709</td>\n",
       "      <td>1</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>2.366883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22390</td>\n",
       "      <td>310000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>32</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>4762</td>\n",
       "      <td>26943</td>\n",
       "      <td>7488</td>\n",
       "      <td>10276</td>\n",
       "      <td>96059</td>\n",
       "      <td>6434</td>\n",
       "      <td>26943</td>\n",
       "      <td>5000</td>\n",
       "      <td>6000</td>\n",
       "      <td>93000</td>\n",
       "      <td>3000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061522</td>\n",
       "      <td>2.361507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6854</td>\n",
       "      <td>290000</td>\n",
       "      <td>male</td>\n",
       "      <td>high school</td>\n",
       "      <td>single</td>\n",
       "      <td>34</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>5451</td>\n",
       "      <td>6230</td>\n",
       "      <td>140802</td>\n",
       "      <td>143354</td>\n",
       "      <td>146225</td>\n",
       "      <td>148820</td>\n",
       "      <td>1200</td>\n",
       "      <td>135000</td>\n",
       "      <td>5200</td>\n",
       "      <td>5500</td>\n",
       "      <td>5500</td>\n",
       "      <td>5400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061900</td>\n",
       "      <td>2.358911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8959</td>\n",
       "      <td>340000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>44</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>83059</td>\n",
       "      <td>85634</td>\n",
       "      <td>73950</td>\n",
       "      <td>59324</td>\n",
       "      <td>156094</td>\n",
       "      <td>110234</td>\n",
       "      <td>20000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>112000</td>\n",
       "      <td>4234</td>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061935</td>\n",
       "      <td>2.358675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1980</td>\n",
       "      <td>500000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>35176</td>\n",
       "      <td>36193</td>\n",
       "      <td>44157</td>\n",
       "      <td>48322</td>\n",
       "      <td>21593</td>\n",
       "      <td>13866</td>\n",
       "      <td>2504</td>\n",
       "      <td>10004</td>\n",
       "      <td>5178</td>\n",
       "      <td>1047</td>\n",
       "      <td>2019</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.062027</td>\n",
       "      <td>2.358041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18899</td>\n",
       "      <td>140000</td>\n",
       "      <td>female</td>\n",
       "      <td>other</td>\n",
       "      <td>single</td>\n",
       "      <td>28</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>108018</td>\n",
       "      <td>6500</td>\n",
       "      <td>6327</td>\n",
       "      <td>138485</td>\n",
       "      <td>140492</td>\n",
       "      <td>141006</td>\n",
       "      <td>1000</td>\n",
       "      <td>6327</td>\n",
       "      <td>135000</td>\n",
       "      <td>4700</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063140</td>\n",
       "      <td>2.350490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2302</td>\n",
       "      <td>230000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>30</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>2212</td>\n",
       "      <td>17402</td>\n",
       "      <td>32450</td>\n",
       "      <td>17285</td>\n",
       "      <td>9766</td>\n",
       "      <td>9981</td>\n",
       "      <td>17402</td>\n",
       "      <td>20013</td>\n",
       "      <td>346</td>\n",
       "      <td>5000</td>\n",
       "      <td>8000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063386</td>\n",
       "      <td>2.348836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>523</td>\n",
       "      <td>360000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>28</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>1210</td>\n",
       "      <td>820</td>\n",
       "      <td>64644</td>\n",
       "      <td>125984</td>\n",
       "      <td>106584</td>\n",
       "      <td>125557</td>\n",
       "      <td>390</td>\n",
       "      <td>75720</td>\n",
       "      <td>62520</td>\n",
       "      <td>17000</td>\n",
       "      <td>132200</td>\n",
       "      <td>167000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063629</td>\n",
       "      <td>2.347206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11745</td>\n",
       "      <td>220000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>51</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>20730</td>\n",
       "      <td>-270</td>\n",
       "      <td>53895</td>\n",
       "      <td>-105</td>\n",
       "      <td>20895</td>\n",
       "      <td>20835</td>\n",
       "      <td>0</td>\n",
       "      <td>54165</td>\n",
       "      <td>0</td>\n",
       "      <td>21000</td>\n",
       "      <td>20940</td>\n",
       "      <td>33460</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063813</td>\n",
       "      <td>2.345973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8339</td>\n",
       "      <td>480000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>58</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>24610</td>\n",
       "      <td>-310</td>\n",
       "      <td>148544</td>\n",
       "      <td>18791</td>\n",
       "      <td>5909</td>\n",
       "      <td>68988</td>\n",
       "      <td>4</td>\n",
       "      <td>149654</td>\n",
       "      <td>18885</td>\n",
       "      <td>5940</td>\n",
       "      <td>69337</td>\n",
       "      <td>200655</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063860</td>\n",
       "      <td>2.345661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22712</td>\n",
       "      <td>320000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>157249</td>\n",
       "      <td>148123</td>\n",
       "      <td>142852</td>\n",
       "      <td>133583</td>\n",
       "      <td>129049</td>\n",
       "      <td>128325</td>\n",
       "      <td>6000</td>\n",
       "      <td>6048</td>\n",
       "      <td>6000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065316</td>\n",
       "      <td>2.336031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26005</td>\n",
       "      <td>320000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>2276</td>\n",
       "      <td>6626</td>\n",
       "      <td>11131</td>\n",
       "      <td>13824</td>\n",
       "      <td>17992</td>\n",
       "      <td>15250</td>\n",
       "      <td>6626</td>\n",
       "      <td>12446</td>\n",
       "      <td>17746</td>\n",
       "      <td>6000</td>\n",
       "      <td>5749</td>\n",
       "      <td>928</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065442</td>\n",
       "      <td>2.335207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13797</td>\n",
       "      <td>390000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>36</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>3931</td>\n",
       "      <td>3625</td>\n",
       "      <td>1600</td>\n",
       "      <td>3815</td>\n",
       "      <td>8330</td>\n",
       "      <td>4765</td>\n",
       "      <td>3625</td>\n",
       "      <td>1600</td>\n",
       "      <td>3315</td>\n",
       "      <td>11645</td>\n",
       "      <td>4765</td>\n",
       "      <td>2171</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065657</td>\n",
       "      <td>2.333801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10147</td>\n",
       "      <td>450000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>46</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>28205</td>\n",
       "      <td>3760</td>\n",
       "      <td>4148</td>\n",
       "      <td>2312</td>\n",
       "      <td>6909</td>\n",
       "      <td>4189</td>\n",
       "      <td>3793</td>\n",
       "      <td>4148</td>\n",
       "      <td>2312</td>\n",
       "      <td>6909</td>\n",
       "      <td>4189</td>\n",
       "      <td>1539</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>2.333313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1199</td>\n",
       "      <td>340000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>single</td>\n",
       "      <td>44</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>142836</td>\n",
       "      <td>145125</td>\n",
       "      <td>146682</td>\n",
       "      <td>150407</td>\n",
       "      <td>147868</td>\n",
       "      <td>149349</td>\n",
       "      <td>7000</td>\n",
       "      <td>5500</td>\n",
       "      <td>6027</td>\n",
       "      <td>5328</td>\n",
       "      <td>5390</td>\n",
       "      <td>6047</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065756</td>\n",
       "      <td>2.333153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10754</td>\n",
       "      <td>160000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>31</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>42781</td>\n",
       "      <td>42774</td>\n",
       "      <td>41817</td>\n",
       "      <td>749</td>\n",
       "      <td>5572</td>\n",
       "      <td>10573</td>\n",
       "      <td>2300</td>\n",
       "      <td>2300</td>\n",
       "      <td>749</td>\n",
       "      <td>5572</td>\n",
       "      <td>5573</td>\n",
       "      <td>13793</td>\n",
       "      <td>1</td>\n",
       "      <td>0.066522</td>\n",
       "      <td>2.328184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25816</td>\n",
       "      <td>350000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>47</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>97500</td>\n",
       "      <td>84202</td>\n",
       "      <td>82933</td>\n",
       "      <td>80501</td>\n",
       "      <td>79038</td>\n",
       "      <td>80694</td>\n",
       "      <td>3010</td>\n",
       "      <td>2970</td>\n",
       "      <td>2886</td>\n",
       "      <td>2824</td>\n",
       "      <td>2925</td>\n",
       "      <td>2987</td>\n",
       "      <td>1</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>2.327775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12668</td>\n",
       "      <td>210000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>37</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>24547</td>\n",
       "      <td>48302</td>\n",
       "      <td>4549</td>\n",
       "      <td>3085</td>\n",
       "      <td>7300</td>\n",
       "      <td>6583</td>\n",
       "      <td>4519</td>\n",
       "      <td>9098</td>\n",
       "      <td>3085</td>\n",
       "      <td>7300</td>\n",
       "      <td>6583</td>\n",
       "      <td>5060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.066960</td>\n",
       "      <td>2.325363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15482</td>\n",
       "      <td>150000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>37</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>22109</td>\n",
       "      <td>10876</td>\n",
       "      <td>10268</td>\n",
       "      <td>5872</td>\n",
       "      <td>3068</td>\n",
       "      <td>2181</td>\n",
       "      <td>10943</td>\n",
       "      <td>10273</td>\n",
       "      <td>5978</td>\n",
       "      <td>3068</td>\n",
       "      <td>2181</td>\n",
       "      <td>3242</td>\n",
       "      <td>1</td>\n",
       "      <td>0.067985</td>\n",
       "      <td>2.318820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8910</th>\n",
       "      <td>2865</td>\n",
       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>46</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>28390</td>\n",
       "      <td>29639</td>\n",
       "      <td>30854</td>\n",
       "      <td>30062</td>\n",
       "      <td>32705</td>\n",
       "      <td>33519</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>3300</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.772660</td>\n",
       "      <td>-1.721227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8911</th>\n",
       "      <td>8115</td>\n",
       "      <td>120000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>26</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>12034</td>\n",
       "      <td>12548</td>\n",
       "      <td>12056</td>\n",
       "      <td>13958</td>\n",
       "      <td>13468</td>\n",
       "      <td>6144</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>57258</td>\n",
       "      <td>0</td>\n",
       "      <td>0.772848</td>\n",
       "      <td>-1.721707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8912</th>\n",
       "      <td>13422</td>\n",
       "      <td>100000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>29</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>74032</td>\n",
       "      <td>75557</td>\n",
       "      <td>76434</td>\n",
       "      <td>74611</td>\n",
       "      <td>79292</td>\n",
       "      <td>80945</td>\n",
       "      <td>3300</td>\n",
       "      <td>2700</td>\n",
       "      <td>0</td>\n",
       "      <td>5900</td>\n",
       "      <td>3100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.774303</td>\n",
       "      <td>-1.725433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8913</th>\n",
       "      <td>12846</td>\n",
       "      <td>90000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>42</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>95773</td>\n",
       "      <td>95489</td>\n",
       "      <td>94681</td>\n",
       "      <td>93965</td>\n",
       "      <td>90545</td>\n",
       "      <td>90529</td>\n",
       "      <td>4000</td>\n",
       "      <td>3500</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "      <td>4200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.775734</td>\n",
       "      <td>-1.729118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914</th>\n",
       "      <td>1629</td>\n",
       "      <td>140000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>31</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>89910</td>\n",
       "      <td>92588</td>\n",
       "      <td>91936</td>\n",
       "      <td>94623</td>\n",
       "      <td>94952</td>\n",
       "      <td>95234</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>5000</td>\n",
       "      <td>1900</td>\n",
       "      <td>4700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776880</td>\n",
       "      <td>-1.732076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8915</th>\n",
       "      <td>4249</td>\n",
       "      <td>90000</td>\n",
       "      <td>male</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>42</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>48674</td>\n",
       "      <td>49895</td>\n",
       "      <td>52570</td>\n",
       "      <td>53614</td>\n",
       "      <td>54534</td>\n",
       "      <td>53374</td>\n",
       "      <td>2300</td>\n",
       "      <td>4116</td>\n",
       "      <td>2500</td>\n",
       "      <td>2052</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.779941</td>\n",
       "      <td>-1.740033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8916</th>\n",
       "      <td>9756</td>\n",
       "      <td>140000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>31</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>51028</td>\n",
       "      <td>52112</td>\n",
       "      <td>55232</td>\n",
       "      <td>55932</td>\n",
       "      <td>54910</td>\n",
       "      <td>57344</td>\n",
       "      <td>2500</td>\n",
       "      <td>4600</td>\n",
       "      <td>2200</td>\n",
       "      <td>0</td>\n",
       "      <td>3513</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.780607</td>\n",
       "      <td>-1.741776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8917</th>\n",
       "      <td>27215</td>\n",
       "      <td>60000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>20195</td>\n",
       "      <td>21267</td>\n",
       "      <td>21332</td>\n",
       "      <td>21680</td>\n",
       "      <td>23011</td>\n",
       "      <td>23498</td>\n",
       "      <td>1700</td>\n",
       "      <td>700</td>\n",
       "      <td>1000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.781268</td>\n",
       "      <td>-1.743508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8918</th>\n",
       "      <td>5986</td>\n",
       "      <td>100000</td>\n",
       "      <td>male</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>44</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>30076</td>\n",
       "      <td>31287</td>\n",
       "      <td>31676</td>\n",
       "      <td>32259</td>\n",
       "      <td>31608</td>\n",
       "      <td>33524</td>\n",
       "      <td>2000</td>\n",
       "      <td>1200</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.784777</td>\n",
       "      <td>-1.752759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8919</th>\n",
       "      <td>15186</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>25</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7593</td>\n",
       "      <td>9634</td>\n",
       "      <td>4476</td>\n",
       "      <td>8830</td>\n",
       "      <td>8153</td>\n",
       "      <td>6422</td>\n",
       "      <td>2379</td>\n",
       "      <td>7</td>\n",
       "      <td>7002</td>\n",
       "      <td>13</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.787675</td>\n",
       "      <td>-1.760476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8920</th>\n",
       "      <td>2974</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>24</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.793444</td>\n",
       "      <td>-1.776054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8921</th>\n",
       "      <td>10785</td>\n",
       "      <td>80000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>33</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>53843</td>\n",
       "      <td>55933</td>\n",
       "      <td>56575</td>\n",
       "      <td>57303</td>\n",
       "      <td>58593</td>\n",
       "      <td>59738</td>\n",
       "      <td>3500</td>\n",
       "      <td>2100</td>\n",
       "      <td>2200</td>\n",
       "      <td>2300</td>\n",
       "      <td>2200</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.794019</td>\n",
       "      <td>-1.777621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8922</th>\n",
       "      <td>13290</td>\n",
       "      <td>230000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>34</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>190784</td>\n",
       "      <td>195724</td>\n",
       "      <td>198707</td>\n",
       "      <td>201634</td>\n",
       "      <td>205949</td>\n",
       "      <td>210077</td>\n",
       "      <td>9300</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.796132</td>\n",
       "      <td>-1.783414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8923</th>\n",
       "      <td>27255</td>\n",
       "      <td>80000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>46</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>40509</td>\n",
       "      <td>40551</td>\n",
       "      <td>42592</td>\n",
       "      <td>43296</td>\n",
       "      <td>43892</td>\n",
       "      <td>43060</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>1700</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.797857</td>\n",
       "      <td>-1.788172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8924</th>\n",
       "      <td>17703</td>\n",
       "      <td>60000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3167</td>\n",
       "      <td>5601</td>\n",
       "      <td>5366</td>\n",
       "      <td>6772</td>\n",
       "      <td>6515</td>\n",
       "      <td>7906</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.802237</td>\n",
       "      <td>-1.800380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8925</th>\n",
       "      <td>13811</td>\n",
       "      <td>40000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>47</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>11084</td>\n",
       "      <td>12605</td>\n",
       "      <td>13102</td>\n",
       "      <td>12595</td>\n",
       "      <td>14386</td>\n",
       "      <td>14005</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.805651</td>\n",
       "      <td>-1.810028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8926</th>\n",
       "      <td>16920</td>\n",
       "      <td>50000</td>\n",
       "      <td>male</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>52</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>36428</td>\n",
       "      <td>37530</td>\n",
       "      <td>38630</td>\n",
       "      <td>41774</td>\n",
       "      <td>40806</td>\n",
       "      <td>41357</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>4086</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.807385</td>\n",
       "      <td>-1.814973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8927</th>\n",
       "      <td>17748</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>54</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>22147</td>\n",
       "      <td>24770</td>\n",
       "      <td>26068</td>\n",
       "      <td>28842</td>\n",
       "      <td>28094</td>\n",
       "      <td>27361</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.812416</td>\n",
       "      <td>-1.829496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>18659</td>\n",
       "      <td>40000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>28</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>31131</td>\n",
       "      <td>33815</td>\n",
       "      <td>33002</td>\n",
       "      <td>32173</td>\n",
       "      <td>34629</td>\n",
       "      <td>33940</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.814786</td>\n",
       "      <td>-1.836434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8929</th>\n",
       "      <td>26565</td>\n",
       "      <td>200000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>55</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>159017</td>\n",
       "      <td>162697</td>\n",
       "      <td>163143</td>\n",
       "      <td>161906</td>\n",
       "      <td>165807</td>\n",
       "      <td>169599</td>\n",
       "      <td>9159</td>\n",
       "      <td>4842</td>\n",
       "      <td>3000</td>\n",
       "      <td>8000</td>\n",
       "      <td>7000</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.815782</td>\n",
       "      <td>-1.839367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930</th>\n",
       "      <td>21098</td>\n",
       "      <td>200000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>42</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>168289</td>\n",
       "      <td>172001</td>\n",
       "      <td>175281</td>\n",
       "      <td>177895</td>\n",
       "      <td>180078</td>\n",
       "      <td>184048</td>\n",
       "      <td>8000</td>\n",
       "      <td>7500</td>\n",
       "      <td>7000</td>\n",
       "      <td>6600</td>\n",
       "      <td>7000</td>\n",
       "      <td>7100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816460</td>\n",
       "      <td>-1.841371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931</th>\n",
       "      <td>7068</td>\n",
       "      <td>90000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>30</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>2450</td>\n",
       "      <td>2150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.825031</td>\n",
       "      <td>-1.867161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8932</th>\n",
       "      <td>3087</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>24</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.825105</td>\n",
       "      <td>-1.867387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8933</th>\n",
       "      <td>14589</td>\n",
       "      <td>280000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>50</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>5 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>327918</td>\n",
       "      <td>321476</td>\n",
       "      <td>314931</td>\n",
       "      <td>176439</td>\n",
       "      <td>154010</td>\n",
       "      <td>134334</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>6267</td>\n",
       "      <td>2257</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832569</td>\n",
       "      <td>-1.890599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8934</th>\n",
       "      <td>16957</td>\n",
       "      <td>270000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>50</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>213616</td>\n",
       "      <td>208784</td>\n",
       "      <td>212058</td>\n",
       "      <td>207226</td>\n",
       "      <td>202394</td>\n",
       "      <td>231339</td>\n",
       "      <td>0</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32236</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.841972</td>\n",
       "      <td>-1.920928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8935</th>\n",
       "      <td>29505</td>\n",
       "      <td>20000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>40</td>\n",
       "      <td>1 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>14829</td>\n",
       "      <td>17267</td>\n",
       "      <td>16706</td>\n",
       "      <td>18694</td>\n",
       "      <td>19049</td>\n",
       "      <td>18459</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2560</td>\n",
       "      <td>955</td>\n",
       "      <td>0</td>\n",
       "      <td>661</td>\n",
       "      <td>0</td>\n",
       "      <td>0.852781</td>\n",
       "      <td>-1.957464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8936</th>\n",
       "      <td>19316</td>\n",
       "      <td>110000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>41</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.866568</td>\n",
       "      <td>-2.007067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8937</th>\n",
       "      <td>22725</td>\n",
       "      <td>100000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>38</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.869051</td>\n",
       "      <td>-2.016405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8938</th>\n",
       "      <td>9672</td>\n",
       "      <td>170000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>48</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.874018</td>\n",
       "      <td>-2.035492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8939</th>\n",
       "      <td>5916</td>\n",
       "      <td>110000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>41</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.886468</td>\n",
       "      <td>-2.085985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8940 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  LIMIT_BAL     SEX        EDUCATION MARRIAGE  AGE  \\\n",
       "0      2561     310000  female  graduate school   single   32   \n",
       "1      3016     350000    male  graduate school  married   38   \n",
       "2     11462     210000  female  graduate school   single   46   \n",
       "3     25772     350000  female  graduate school  married   33   \n",
       "4      6933     500000    male  graduate school   single   37   \n",
       "5     22505     260000  female       university   single   33   \n",
       "6     22751     350000  female  graduate school  married   32   \n",
       "7     13381     400000  female       university   single   35   \n",
       "8     19530     350000  female       university  married   36   \n",
       "9     15549     450000    male       university   single   36   \n",
       "10    25692     330000  female  graduate school   single   42   \n",
       "11      971     300000    male  graduate school  married   42   \n",
       "12    22390     310000  female  graduate school  married   32   \n",
       "13     6854     290000    male      high school   single   34   \n",
       "14     8959     340000    male  graduate school   single   44   \n",
       "15     1980     500000  female  graduate school  married   35   \n",
       "16    18899     140000  female            other   single   28   \n",
       "17     2302     230000  female  graduate school  married   30   \n",
       "18      523     360000    male  graduate school   single   28   \n",
       "19    11745     220000    male  graduate school   single   51   \n",
       "20     8339     480000    male  graduate school  married   58   \n",
       "21    22712     320000  female      high school  married   35   \n",
       "22    26005     320000  female       university  married   35   \n",
       "23    13797     390000    male  graduate school  married   36   \n",
       "24    10147     450000  female  graduate school  married   46   \n",
       "25     1199     340000  female      high school   single   44   \n",
       "26    10754     160000  female       university  married   31   \n",
       "27    25816     350000  female       university  married   47   \n",
       "28    12668     210000  female  graduate school  married   37   \n",
       "29    15482     150000    male  graduate school  married   37   \n",
       "...     ...        ...     ...              ...      ...  ...   \n",
       "8910   2865      50000  female       university  married   46   \n",
       "8911   8115     120000  female       university   single   26   \n",
       "8912  13422     100000  female  graduate school   single   29   \n",
       "8913  12846      90000    male       university  married   42   \n",
       "8914   1629     140000  female      high school  married   31   \n",
       "8915   4249      90000    male      high school  married   42   \n",
       "8916   9756     140000    male  graduate school  married   31   \n",
       "8917  27215      60000    male       university  married   35   \n",
       "8918   5986     100000    male      high school  married   44   \n",
       "8919  15186      30000  female  graduate school   single   25   \n",
       "8920   2974      30000  female       university  married   24   \n",
       "8921  10785      80000  female       university  married   33   \n",
       "8922  13290     230000  female  graduate school  married   34   \n",
       "8923  27255      80000    male  graduate school  married   46   \n",
       "8924  17703      60000  female       university  married   35   \n",
       "8925  13811      40000    male  graduate school  married   47   \n",
       "8926  16920      50000    male      high school  married   52   \n",
       "8927  17748      30000  female      high school  married   54   \n",
       "8928  18659      40000  female       university  married   28   \n",
       "8929  26565     200000  female      high school  married   55   \n",
       "8930  21098     200000    male  graduate school  married   42   \n",
       "8931   7068      90000  female  graduate school   single   30   \n",
       "8932   3087      30000  female       university   single   24   \n",
       "8933  14589     280000    male  graduate school  married   50   \n",
       "8934  16957     270000    male  graduate school  married   50   \n",
       "8935  29505      20000    male       university  married   40   \n",
       "8936  19316     110000  female  graduate school  married   41   \n",
       "8937  22725     100000  female       university  married   38   \n",
       "8938   9672     170000    male  graduate school   single   48   \n",
       "8939   5916     110000  female  graduate school  married   41   \n",
       "\n",
       "                        PAY_0                    PAY_2  \\\n",
       "0              no consumption           no consumption   \n",
       "1              no consumption           no consumption   \n",
       "2                    pay duly                 pay duly   \n",
       "3     use of revolving credit                 pay duly   \n",
       "4                    pay duly                 pay duly   \n",
       "5                    pay duly                 pay duly   \n",
       "6                    pay duly                 pay duly   \n",
       "7     use of revolving credit  use of revolving credit   \n",
       "8     use of revolving credit  use of revolving credit   \n",
       "9              no consumption           no consumption   \n",
       "10             no consumption           no consumption   \n",
       "11                   pay duly  use of revolving credit   \n",
       "12    use of revolving credit                 pay duly   \n",
       "13    use of revolving credit  use of revolving credit   \n",
       "14    use of revolving credit  use of revolving credit   \n",
       "15    use of revolving credit  use of revolving credit   \n",
       "16    use of revolving credit  use of revolving credit   \n",
       "17                   pay duly                 pay duly   \n",
       "18                   pay duly                 pay duly   \n",
       "19                   pay duly                 pay duly   \n",
       "20             no consumption           no consumption   \n",
       "21    use of revolving credit  use of revolving credit   \n",
       "22                   pay duly                 pay duly   \n",
       "23             no consumption           no consumption   \n",
       "24                   pay duly                 pay duly   \n",
       "25    use of revolving credit  use of revolving credit   \n",
       "26    use of revolving credit  use of revolving credit   \n",
       "27    use of revolving credit  use of revolving credit   \n",
       "28    use of revolving credit  use of revolving credit   \n",
       "29             no consumption           no consumption   \n",
       "...                       ...                      ...   \n",
       "8910            2 month delay            2 month delay   \n",
       "8911            3 month delay            3 month delay   \n",
       "8912            2 month delay            2 month delay   \n",
       "8913            2 month delay            2 month delay   \n",
       "8914            2 month delay            2 month delay   \n",
       "8915            2 month delay            2 month delay   \n",
       "8916            2 month delay  use of revolving credit   \n",
       "8917            2 month delay            2 month delay   \n",
       "8918            2 month delay            2 month delay   \n",
       "8919            2 month delay            2 month delay   \n",
       "8920            2 month delay            2 month delay   \n",
       "8921            2 month delay            2 month delay   \n",
       "8922            2 month delay            2 month delay   \n",
       "8923            2 month delay            2 month delay   \n",
       "8924            2 month delay            2 month delay   \n",
       "8925            2 month delay            2 month delay   \n",
       "8926            2 month delay            2 month delay   \n",
       "8927            2 month delay            2 month delay   \n",
       "8928            2 month delay            2 month delay   \n",
       "8929            2 month delay            2 month delay   \n",
       "8930            2 month delay            2 month delay   \n",
       "8931            2 month delay            2 month delay   \n",
       "8932            2 month delay            2 month delay   \n",
       "8933            3 month delay            5 month delay   \n",
       "8934            2 month delay            4 month delay   \n",
       "8935            1 month delay            2 month delay   \n",
       "8936            3 month delay            2 month delay   \n",
       "8937            3 month delay            2 month delay   \n",
       "8938            2 month delay            2 month delay   \n",
       "8939            2 month delay            2 month delay   \n",
       "\n",
       "                        PAY_3                    PAY_4  \\\n",
       "0              no consumption           no consumption   \n",
       "1                    pay duly  use of revolving credit   \n",
       "2                    pay duly  use of revolving credit   \n",
       "3                    pay duly                 pay duly   \n",
       "4                    pay duly                 pay duly   \n",
       "5                    pay duly                 pay duly   \n",
       "6              no consumption           no consumption   \n",
       "7     use of revolving credit  use of revolving credit   \n",
       "8     use of revolving credit  use of revolving credit   \n",
       "9              no consumption           no consumption   \n",
       "10             no consumption           no consumption   \n",
       "11    use of revolving credit                 pay duly   \n",
       "12    use of revolving credit  use of revolving credit   \n",
       "13    use of revolving credit  use of revolving credit   \n",
       "14    use of revolving credit  use of revolving credit   \n",
       "15    use of revolving credit  use of revolving credit   \n",
       "16                   pay duly  use of revolving credit   \n",
       "17    use of revolving credit  use of revolving credit   \n",
       "18                   pay duly  use of revolving credit   \n",
       "19                   pay duly                 pay duly   \n",
       "20             no consumption           no consumption   \n",
       "21    use of revolving credit  use of revolving credit   \n",
       "22                   pay duly                 pay duly   \n",
       "23             no consumption           no consumption   \n",
       "24                   pay duly                 pay duly   \n",
       "25    use of revolving credit  use of revolving credit   \n",
       "26    use of revolving credit                 pay duly   \n",
       "27    use of revolving credit  use of revolving credit   \n",
       "28                   pay duly                 pay duly   \n",
       "29             no consumption           no consumption   \n",
       "...                       ...                      ...   \n",
       "8910            2 month delay            2 month delay   \n",
       "8911            2 month delay            2 month delay   \n",
       "8912            2 month delay            2 month delay   \n",
       "8913            2 month delay            2 month delay   \n",
       "8914            2 month delay            2 month delay   \n",
       "8915            2 month delay            3 month delay   \n",
       "8916  use of revolving credit            2 month delay   \n",
       "8917            2 month delay            2 month delay   \n",
       "8918            2 month delay            2 month delay   \n",
       "8919            2 month delay            2 month delay   \n",
       "8920            2 month delay            2 month delay   \n",
       "8921            2 month delay            2 month delay   \n",
       "8922            2 month delay            2 month delay   \n",
       "8923            2 month delay            2 month delay   \n",
       "8924            2 month delay            2 month delay   \n",
       "8925            2 month delay            2 month delay   \n",
       "8926            2 month delay            2 month delay   \n",
       "8927            2 month delay            2 month delay   \n",
       "8928            3 month delay            2 month delay   \n",
       "8929            3 month delay            2 month delay   \n",
       "8930            2 month delay            2 month delay   \n",
       "8931            3 month delay            3 month delay   \n",
       "8932            7 month delay            7 month delay   \n",
       "8933            4 month delay            3 month delay   \n",
       "8934            3 month delay            3 month delay   \n",
       "8935            3 month delay            2 month delay   \n",
       "8936            2 month delay            7 month delay   \n",
       "8937            2 month delay            3 month delay   \n",
       "8938            7 month delay            7 month delay   \n",
       "8939            7 month delay            7 month delay   \n",
       "\n",
       "                        PAY_5                    PAY_6  BILL_AMT1  BILL_AMT2  \\\n",
       "0              no consumption           no consumption      20138       8267   \n",
       "1     use of revolving credit           no consumption      16459       4120   \n",
       "2     use of revolving credit                 pay duly      15655       3918   \n",
       "3                    pay duly                 pay duly      82964      68532   \n",
       "4                    pay duly                 pay duly       4331      60446   \n",
       "5                    pay duly  use of revolving credit       5188      12357   \n",
       "6              no consumption           no consumption      30625      60003   \n",
       "7     use of revolving credit  use of revolving credit     109943     222085   \n",
       "8     use of revolving credit  use of revolving credit      21026      35588   \n",
       "9              no consumption           no consumption       8012       4009   \n",
       "10             no consumption           no consumption        565      20650   \n",
       "11    use of revolving credit  use of revolving credit      11973      61834   \n",
       "12    use of revolving credit  use of revolving credit       4762      26943   \n",
       "13    use of revolving credit  use of revolving credit       5451       6230   \n",
       "14    use of revolving credit  use of revolving credit      83059      85634   \n",
       "15    use of revolving credit  use of revolving credit      35176      36193   \n",
       "16    use of revolving credit  use of revolving credit     108018       6500   \n",
       "17    use of revolving credit  use of revolving credit       2212      17402   \n",
       "18    use of revolving credit                 pay duly       1210        820   \n",
       "19                   pay duly                 pay duly      20730       -270   \n",
       "20             no consumption           no consumption      24610       -310   \n",
       "21    use of revolving credit  use of revolving credit     157249     148123   \n",
       "22    use of revolving credit  use of revolving credit       2276       6626   \n",
       "23                   pay duly                 pay duly       3931       3625   \n",
       "24                   pay duly                 pay duly      28205       3760   \n",
       "25    use of revolving credit  use of revolving credit     142836     145125   \n",
       "26                   pay duly  use of revolving credit      42781      42774   \n",
       "27    use of revolving credit  use of revolving credit      97500      84202   \n",
       "28                   pay duly                 pay duly      24547      48302   \n",
       "29             no consumption           no consumption      22109      10876   \n",
       "...                       ...                      ...        ...        ...   \n",
       "8910            2 month delay            2 month delay      28390      29639   \n",
       "8911            3 month delay            2 month delay      12034      12548   \n",
       "8912            2 month delay            2 month delay      74032      75557   \n",
       "8913            2 month delay  use of revolving credit      95773      95489   \n",
       "8914            2 month delay            2 month delay      89910      92588   \n",
       "8915            3 month delay            3 month delay      48674      49895   \n",
       "8916            2 month delay            2 month delay      51028      52112   \n",
       "8917            2 month delay            2 month delay      20195      21267   \n",
       "8918            2 month delay            2 month delay      30076      31287   \n",
       "8919            2 month delay            2 month delay       7593       9634   \n",
       "8920            2 month delay            2 month delay        150        150   \n",
       "8921            2 month delay            2 month delay      53843      55933   \n",
       "8922            2 month delay            2 month delay     190784     195724   \n",
       "8923            2 month delay            2 month delay      40509      40551   \n",
       "8924            2 month delay            2 month delay       3167       5601   \n",
       "8925            2 month delay            2 month delay      11084      12605   \n",
       "8926            4 month delay            3 month delay      36428      37530   \n",
       "8927            4 month delay            3 month delay      22147      24770   \n",
       "8928            2 month delay            2 month delay      31131      33815   \n",
       "8929            2 month delay            2 month delay     159017     162697   \n",
       "8930            2 month delay            2 month delay     168289     172001   \n",
       "8931            3 month delay            3 month delay        750        750   \n",
       "8932            7 month delay            7 month delay        300        300   \n",
       "8933            2 month delay  use of revolving credit     327918     321476   \n",
       "8934            2 month delay            2 month delay     213616     208784   \n",
       "8935            3 month delay            3 month delay      14829      17267   \n",
       "8936            7 month delay            7 month delay        150        150   \n",
       "8937            3 month delay            3 month delay        750        750   \n",
       "8938            7 month delay            7 month delay       2400       2400   \n",
       "8939            7 month delay            7 month delay        150        150   \n",
       "\n",
       "      BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0         65993       8543       1695        750      8267     66008   \n",
       "1         44164      35233        884       9924       941     44743   \n",
       "2         29881      24247      21664       1556      4854     30366   \n",
       "3         17926      17966      30741      31088     68940     18018   \n",
       "4         30592     154167      13410      25426     60446     30594   \n",
       "5         28656       7497       7685      15434     13000     29022   \n",
       "6          7147       9950      22117       4874     60396      7147   \n",
       "7        223350     213831     210563     211925    120018     10071   \n",
       "8         38002      40357      43663      52735     15000      3000   \n",
       "9          5226       4715       3275       6422      4021      5241   \n",
       "10        15360          0      12923       1816     20650     15360   \n",
       "11        25145      37666      19453      10492     20979      5000   \n",
       "12         7488      10276      96059       6434     26943      5000   \n",
       "13       140802     143354     146225     148820      1200    135000   \n",
       "14        73950      59324     156094     110234     20000      5000   \n",
       "15        44157      48322      21593      13866      2504     10004   \n",
       "16         6327     138485     140492     141006      1000      6327   \n",
       "17        32450      17285       9766       9981     17402     20013   \n",
       "18        64644     125984     106584     125557       390     75720   \n",
       "19        53895       -105      20895      20835         0     54165   \n",
       "20       148544      18791       5909      68988         4    149654   \n",
       "21       142852     133583     129049     128325      6000      6048   \n",
       "22        11131      13824      17992      15250      6626     12446   \n",
       "23         1600       3815       8330       4765      3625      1600   \n",
       "24         4148       2312       6909       4189      3793      4148   \n",
       "25       146682     150407     147868     149349      7000      5500   \n",
       "26        41817        749       5572      10573      2300      2300   \n",
       "27        82933      80501      79038      80694      3010      2970   \n",
       "28         4549       3085       7300       6583      4519      9098   \n",
       "29        10268       5872       3068       2181     10943     10273   \n",
       "...         ...        ...        ...        ...       ...       ...   \n",
       "8910      30854      30062      32705      33519      2000      2000   \n",
       "8911      12056      13958      13468       6144      1000         0   \n",
       "8912      76434      74611      79292      80945      3300      2700   \n",
       "8913      94681      93965      90545      90529      4000      3500   \n",
       "8914      91936      94623      94952      95234      5000      1800   \n",
       "8915      52570      53614      54534      53374      2300      4116   \n",
       "8916      55232      55932      54910      57344      2500      4600   \n",
       "8917      21332      21680      23011      23498      1700       700   \n",
       "8918      31676      32259      31608      33524      2000      1200   \n",
       "8919       4476       8830       8153       6422      2379         7   \n",
       "8920        150        150        150        300         0         0   \n",
       "8921      56575      57303      58593      59738      3500      2100   \n",
       "8922     198707     201634     205949     210077      9300      7500   \n",
       "8923      42592      43296      43892      43060      1000      3000   \n",
       "8924       5366       6772       6515       7906      2500         0   \n",
       "8925      13102      12595      14386      14005      2000      1000   \n",
       "8926      38630      41774      40806      41357      2000      2000   \n",
       "8927      26068      28842      28094      27361      3000      2000   \n",
       "8928      33002      32173      34629      33940      3500         0   \n",
       "8929     163143     161906     165807     169599      9159      4842   \n",
       "8930     175281     177895     180078     184048      8000      7500   \n",
       "8931        750        750       2450       2150         0         0   \n",
       "8932        300        300        300        300         0         0   \n",
       "8933     314931     176439     154010     134334         0         0   \n",
       "8934     212058     207226     202394     231339         0      8000   \n",
       "8935      16706      18694      19049      18459      3000         0   \n",
       "8936        150        150        150        150         0         0   \n",
       "8937        750        750        750        750         0         0   \n",
       "8938       2400       2400       2400       2400         0         0   \n",
       "8939        150        150        150        150         0         0   \n",
       "\n",
       "      PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT_NEXT_MONTH  \\\n",
       "0         8543      1695       750      7350                   1   \n",
       "1            0       884      9924     10824                   1   \n",
       "2            0       433      1556     14047                   1   \n",
       "3        18058     30897     31244     88461                   1   \n",
       "4       150843    163881     25426     39526                   1   \n",
       "5         7500     27769     12000      6200                   1   \n",
       "6         9950     22117      4874         0                   1   \n",
       "7         8037      8018      8809      5022                   1   \n",
       "8         3000      4000     10000     25000                   1   \n",
       "9         4729      3284      6441      4285                   1   \n",
       "10           0     12923      1816     17050                   1   \n",
       "11       37676      8808      2000      2709                   1   \n",
       "12        6000     93000      3000      5000                   1   \n",
       "13        5200      5500      5500      5400                   1   \n",
       "14        2000    112000      4234      4000                   1   \n",
       "15        5178      1047      2019      1004                   1   \n",
       "16      135000      4700      5000      5000                   1   \n",
       "17         346      5000      8000      5000                   1   \n",
       "18       62520     17000    132200    167000                   1   \n",
       "19           0     21000     20940     33460                   1   \n",
       "20       18885      5940     69337    200655                   1   \n",
       "21        6000      5000      5000      5000                   1   \n",
       "22       17746      6000      5749       928                   1   \n",
       "23        3315     11645      4765      2171                   1   \n",
       "24        2312      6909      4189      1539                   1   \n",
       "25        6027      5328      5390      6047                   1   \n",
       "26         749      5572      5573     13793                   1   \n",
       "27        2886      2824      2925      2987                   1   \n",
       "28        3085      7300      6583      5060                   1   \n",
       "29        5978      3068      2181      3242                   1   \n",
       "...        ...       ...       ...       ...                 ...   \n",
       "8910         0      3300      1500         0                   0   \n",
       "8911      2400       100         0     57258                   0   \n",
       "8912         0      5900      3100         0                   0   \n",
       "8913      3500         0      3500      4200                   0   \n",
       "8914      5000      1900      4700         0                   0   \n",
       "8915      2500      2052         0         0                   0   \n",
       "8916      2200         0      3513      3000                   0   \n",
       "8917      1000      2000      1000         0                   0   \n",
       "8918      1400         0      2600         0                   0   \n",
       "8919      7002        13       155         1                   0   \n",
       "8920         0         0       150         0                   0   \n",
       "8921      2200      2300      2200      2100                   0   \n",
       "8922      7500      7500      7500      7600                   0   \n",
       "8923      1700      1600         0      3500                   0   \n",
       "8924      1500         0      1500         0                   0   \n",
       "8925         0      2000         0      2000                   0   \n",
       "8926      4086         0      1500      1000                   0   \n",
       "8927      3500         0         0      1000                   0   \n",
       "8928         0      3000         0      2000                   0   \n",
       "8929      3000      8000      7000      3000                   0   \n",
       "8930      7000      6600      7000      7100                   0   \n",
       "8931         0      2000         0         0                   0   \n",
       "8932         0         0         0         0                   0   \n",
       "8933       500         0      6267      2257                   0   \n",
       "8934         0         0     32236      3000                   0   \n",
       "8935      2560       955         0       661                   0   \n",
       "8936         0         0         0         0                   0   \n",
       "8937         0         0         0      1500                   0   \n",
       "8938         0         0         0         0                   0   \n",
       "8939         0         0         0         0                   0   \n",
       "\n",
       "      p_DEFAULT_NEXT_MONTH  r_DEFAULT_NEXT_MONTH  \n",
       "0                 0.045837              2.483007  \n",
       "1                 0.050230              2.445869  \n",
       "2                 0.050527              2.443456  \n",
       "3                 0.051503              2.435615  \n",
       "4                 0.051717              2.433910  \n",
       "5                 0.053061              2.423347  \n",
       "6                 0.056650              2.396193  \n",
       "7                 0.058753              2.380929  \n",
       "8                 0.058911              2.379801  \n",
       "9                 0.059060              2.378739  \n",
       "10                0.059089              2.378531  \n",
       "11                0.060745              2.366883  \n",
       "12                0.061522              2.361507  \n",
       "13                0.061900              2.358911  \n",
       "14                0.061935              2.358675  \n",
       "15                0.062027              2.358041  \n",
       "16                0.063140              2.350490  \n",
       "17                0.063386              2.348836  \n",
       "18                0.063629              2.347206  \n",
       "19                0.063813              2.345973  \n",
       "20                0.063860              2.345661  \n",
       "21                0.065316              2.336031  \n",
       "22                0.065442              2.335207  \n",
       "23                0.065657              2.333801  \n",
       "24                0.065732              2.333313  \n",
       "25                0.065756              2.333153  \n",
       "26                0.066522              2.328184  \n",
       "27                0.066585              2.327775  \n",
       "28                0.066960              2.325363  \n",
       "29                0.067985              2.318820  \n",
       "...                    ...                   ...  \n",
       "8910              0.772660             -1.721227  \n",
       "8911              0.772848             -1.721707  \n",
       "8912              0.774303             -1.725433  \n",
       "8913              0.775734             -1.729118  \n",
       "8914              0.776880             -1.732076  \n",
       "8915              0.779941             -1.740033  \n",
       "8916              0.780607             -1.741776  \n",
       "8917              0.781268             -1.743508  \n",
       "8918              0.784777             -1.752759  \n",
       "8919              0.787675             -1.760476  \n",
       "8920              0.793444             -1.776054  \n",
       "8921              0.794019             -1.777621  \n",
       "8922              0.796132             -1.783414  \n",
       "8923              0.797857             -1.788172  \n",
       "8924              0.802237             -1.800380  \n",
       "8925              0.805651             -1.810028  \n",
       "8926              0.807385             -1.814973  \n",
       "8927              0.812416             -1.829496  \n",
       "8928              0.814786             -1.836434  \n",
       "8929              0.815782             -1.839367  \n",
       "8930              0.816460             -1.841371  \n",
       "8931              0.825031             -1.867161  \n",
       "8932              0.825105             -1.867387  \n",
       "8933              0.832569             -1.890599  \n",
       "8934              0.841972             -1.920928  \n",
       "8935              0.852781             -1.957464  \n",
       "8936              0.866568             -2.007067  \n",
       "8937              0.869051             -2.016405  \n",
       "8938              0.874018             -2.035492  \n",
       "8939              0.886468             -2.085985  \n",
       "\n",
       "[8940 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_yhat = test_yhat.sort_values(by='r_DEFAULT_NEXT_MONTH', ascending=False).reset_index(drop=True)\n",
    "test_yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple analysis has uncovered some of the most difficult customers for the GBM to correctly predict default. Perhaps because of the high importance of the payment features, `PAY_0`-`PAY_6`, the GBM struggles to correctly predict several cases in which customers made timely recent payments and then suddenly defaulted (high positive residuals) and those customers that were chronically late making payments but did not default (high negative residuals)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot residuals by most important input variable \n",
    "Residuals can also be plotted for important input variables to understand how the values of a single input variable affect prediction errors. When plotted by `PAY_0`, the residuals confirm that the GBM is struggling to accurately predict cases where default status is not correlated with recent payment behavior in an obvious way. The residual plots for values of `PAY_0` indicating timely payment behavior (e.g., `use of revolving credit`, `pay duly`, and `no consumption`) generally display the highest positive residuals and relatively small negative residuals. Residuals for the other values of `PAY_0`, those that represent late recent payments, tend to show large negative residuals and relatively small positive residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f737475a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use Seaborn FacetGrid for convenience\n",
    "g = sns.FacetGrid(test_yhat, row='PAY_0', hue=y)\n",
    "_ = g.map(plt.scatter, yhat, 'r_DEFAULT_NEXT_MONTH', alpha=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Retrain GBM classifier based on results of residual analysis\n",
    "Now that an issue has been discovered using residual analysis, can it be resolved? \n",
    "\n",
    "#### Create a feature that contains information about behavior over time\n",
    "One strategy to improve prediction accuracy is to introduce a new feature that summarizes a customer's spending behavior over time to expose any potential financial instability: the standard deviation of a customer's bill amounts over six months. Pandas has a one-liner for calculating standard deviations for a set of columns, so the H2OFrame is casted back into Pandas DataFrame for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEFAULT_NEXT_MONTH</th>\n",
       "      <th>bill_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>24</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>no consumption</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1761.633219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>26</td>\n",
       "      <td>pay duly</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>637.967841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>34</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>use of revolving credit</td>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>6064.518593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL     SEX   EDUCATION MARRIAGE  AGE                    PAY_0  \\\n",
       "0   1      20000  female  university  married   24            2 month delay   \n",
       "1   2     120000  female  university   single   26                 pay duly   \n",
       "2   3      90000  female  university   single   34  use of revolving credit   \n",
       "\n",
       "                     PAY_2                    PAY_3                    PAY_4  \\\n",
       "0            2 month delay                 pay duly                 pay duly   \n",
       "1            2 month delay  use of revolving credit  use of revolving credit   \n",
       "2  use of revolving credit  use of revolving credit  use of revolving credit   \n",
       "\n",
       "                     PAY_5                    PAY_6  BILL_AMT1  BILL_AMT2  \\\n",
       "0           no consumption           no consumption       3913       3102   \n",
       "1  use of revolving credit            2 month delay       2682       1725   \n",
       "2  use of revolving credit  use of revolving credit      29239      14027   \n",
       "\n",
       "   BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0        689          0          0          0         0       689         0   \n",
       "1       2682       3272       3455       3261         0      1000      1000   \n",
       "2      13559      14331      14948      15549      1518      1500      1000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT_NEXT_MONTH     bill_std  \n",
       "0         0         0         0                   1  1761.633219  \n",
       "1      1000         0      2000                   1   637.967841  \n",
       "2      1000      1000      5000                   0  6064.518593  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.as_data_frame()\n",
    "data['bill_std'] = data[['BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']].std(axis=1)\n",
    "data.head(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Pandas DataFrame back to H2OFrame for modeling\n",
    "To retrain the model with the new feature, an H2OFrame is required and that H2OFrame is split using the same proportion and random seed as in cell 8 for the first GBM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "data = h2o.H2OFrame(data)                          # convert \n",
    "data[y] = data[y].asfactor()                       # ensure target is handled as a categorical variable\n",
    "train, test = data.split_frame([0.7], seed=12345)  # split into training and validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrain GBM with new feature\n",
    "The `train()` function is used to retrain the GBM model with the nearly same hyperparameters used previously in cell 9. A slight, but noticable, increase in accuracy results from retraining with the new feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "GBM Test AUC = 0.7825\n"
     ]
    }
   ],
   "source": [
    "# initialize GBM model\n",
    "model = H2OGradientBoostingEstimator(ntrees=150,            # maximum 150 trees in GBM\n",
    "                                     max_depth=6,           # trees can have maximum depth of 6\n",
    "                                     sample_rate=0.9,       # use 90% of rows in each iteration (tree)\n",
    "                                     col_sample_rate=0.85,  # use 90% of variables in each iteration (tree)\n",
    "                                     stopping_rounds=5,     # stop if validation error does not decrease for 5 iterations (trees)\n",
    "                                     seed=12345)            # for reproducibility\n",
    "\n",
    "# retrain GBM model\n",
    "model.train(y=y,\n",
    "            x=X + ['bill_std'], # add new feature\n",
    "            training_frame=train, \n",
    "            validation_frame=test)\n",
    "\n",
    "# print AUC\n",
    "print('GBM Test AUC = %.4f' % model.auc(valid=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While there maybe be other more complex features or a more optimal set of hyperparameters that could lead to further incremental increases in accuracy, more information is needed to achieve meaningful improvement in prediction performance. In particular, a common measure for credit lending, the customers' debt-to-income ratio, for each payment and billing period could be particularly useful. Spikes in debt-to-income ratio, representing loss of income or large increases in debt, would likely be very indicative of a default and would expose the GBM to information not currently available in the UCI credit card default data. Introducing new data could also de-emphasize `PAY_0`, which would likely result in a more stable model as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Perform sensitivity analysis to test model performance on unseen data\n",
    "\n",
    "Sensitivity analysis investigates whether model behavior and outputs remain stable when data is intentionally perturbed or other changes are simulated in data. Beyond traditional assessment practices, sensitivity analysis of machine learning model predictions is perhaps the most important validation technique for machine learning models. Machine learning models can make drastically differing predictions for only minor changes in input variable values. In practice, many linear model validation techniques focus on the numerical instability of regression parameters due to correlation between input variables or between input variables and the dependent variable. It may be prudent for those switching from linear modeling techniques to machine learning techniques to focus less on numerical instability of model parameters and to focus more on the potential instability of model predictions.\n",
    "\n",
    "Here sensitivity analysis is used to understand the impact of  changing the most important input variable, `PAY_0`, and the impact of a sociologically sensitive variable, `SEX`, in the model. If the model changes in reasonable and expected ways when important variable values are changed this can enhance trust in the model. If the contribution of potentially sensitive variables, such as those related to gender, race, age, marital status, or disability status, can be shown to have minimal impact on the model, this is an indication of fairness in the model predictions and can also increase overall trust in the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bind new model predictions onto test data \n",
    "Typically, a productive exercise in model debugging and validation is to investigate customers with very high or low predicted probabilities to determine if their predictions stay within reasonable bounds when important variables are changed. The predictions from the new, more accurate model are merged onto the test set to find these potentially interesting customers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "preds2 = model.predict(test).drop(['predict', 'p0'])\n",
    "preds2.columns = [yhat]\n",
    "test_yhat = test.cbind(preds1[yhat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper function for finding percentile indices\n",
    "The function below finds and returns the row indices for the minimum, the maximum, and the deciles of one column in terms of another -- in this case, the model predictions (`p_DEFAULT_NEXT_MONTH`) and the row identifier (`ID`), respectively. These indices are used as a starting point for boundary testing. Outlying predictions found through residual analysis is another group of potentially interesting local predictions to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 28716,\n",
       " 10: 8942,\n",
       " 20: 28257,\n",
       " 30: 4074,\n",
       " 40: 13411,\n",
       " 50: 16633,\n",
       " 60: 2402,\n",
       " 70: 19769,\n",
       " 80: 25069,\n",
       " 90: 21372,\n",
       " 99: 29116}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_percentile_dict(yhat, id_, frame):\n",
    "\n",
    "    \"\"\" Returns the minimum, the maximum, and the deciles of a column, yhat, \n",
    "        as the indices based on another column id_.\n",
    "    \n",
    "    Args:\n",
    "        yhat: Column in which to find percentiles.\n",
    "        id_: Id column that stores indices for percentiles of yhat.\n",
    "        frame: H2OFrame containing yhat and id_. \n",
    "    \n",
    "    Returns:\n",
    "        Dictionary of percentile values and index column values.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # create a copy of frame and sort it by yhat\n",
    "    sort_df = frame.as_data_frame()\n",
    "    sort_df.sort_values(yhat, inplace=True)\n",
    "    sort_df.reset_index(inplace=True)\n",
    "    \n",
    "    # find top and bottom percentiles\n",
    "    percentiles_dict = {}\n",
    "    percentiles_dict[0] = sort_df.loc[0, id_]\n",
    "    percentiles_dict[99] = sort_df.loc[sort_df.shape[0]-1, id_]\n",
    "\n",
    "    # find 10th-90th percentiles\n",
    "    inc = sort_df.shape[0]//10\n",
    "    for i in range(1, 10):\n",
    "        percentiles_dict[i * 10] = sort_df.loc[i * inc,  id_]\n",
    "\n",
    "    return percentiles_dict\n",
    "\n",
    "# display percentiles dictionary\n",
    "# ID values for rows\n",
    "# from lowest prediction \n",
    "# to highest prediction\n",
    "pred_percentile_dict = get_percentile_dict(yhat, 'ID', test_yhat)\n",
    "pred_percentile_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display test data prediction range\n",
    "Unlike some regression models and neural networks that can produce outrageous predictions for changes in input variables, GBM predictions in new data are bounded by the lowest and highest probability leaf nodes in each constiuent decision tree in the trained model. While unbounded, extreme predictions are typically not an issue for tree models and classification tasks, it is often a good idea to check that the model predictions cover a full range of useful values in the test set. Below, we can see that the model produces both low and high predictions in the test set, indicating that it is likely responsive to signal in new data and not simply predicting the majority class or an average value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest prediction: "
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  p_DEFAULT_NEXT_MONTH</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">             0.0383668</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Highest prediction: "
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  p_DEFAULT_NEXT_MONTH</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">              0.895285</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print('Lowest prediction:', test_yhat[test_yhat['ID'] == int(pred_percentile_dict[0])][[y, yhat]])\n",
    "print('Highest prediction:', test_yhat[test_yhat['ID'] == int(pred_percentile_dict[99])][[y, yhat]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use trained model to test predictions for interesting situations: customer least likely to default\n",
    "As a starting point for further analysis, sensitivity analysis is performed for the customer least likely to default. This woman has a very low probability of defaulting according to the trained GBM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">   ID</th><th style=\"text-align: right;\">  LIMIT_BAL</th><th>SEX   </th><th>EDUCATION  </th><th>MARRIAGE  </th><th style=\"text-align: right;\">  AGE</th><th>PAY_0         </th><th>PAY_2         </th><th>PAY_3         </th><th>PAY_4         </th><th>PAY_5         </th><th>PAY_6         </th><th style=\"text-align: right;\">  BILL_AMT1</th><th style=\"text-align: right;\">  BILL_AMT2</th><th style=\"text-align: right;\">  BILL_AMT3</th><th style=\"text-align: right;\">  BILL_AMT4</th><th style=\"text-align: right;\">  BILL_AMT5</th><th style=\"text-align: right;\">  BILL_AMT6</th><th style=\"text-align: right;\">  PAY_AMT1</th><th style=\"text-align: right;\">  PAY_AMT2</th><th style=\"text-align: right;\">  PAY_AMT3</th><th style=\"text-align: right;\">  PAY_AMT4</th><th style=\"text-align: right;\">  PAY_AMT5</th><th style=\"text-align: right;\">  PAY_AMT6</th><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  bill_std</th><th style=\"text-align: right;\">  p_DEFAULT_NEXT_MONTH</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">28716</td><td style=\"text-align: right;\">     780000</td><td>female</td><td>university </td><td>single    </td><td style=\"text-align: right;\">   41</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td style=\"text-align: right;\">     101957</td><td style=\"text-align: right;\">      61715</td><td style=\"text-align: right;\">      38686</td><td style=\"text-align: right;\">      21482</td><td style=\"text-align: right;\">      72628</td><td style=\"text-align: right;\">     182792</td><td style=\"text-align: right;\">     62819</td><td style=\"text-align: right;\">     39558</td><td style=\"text-align: right;\">     22204</td><td style=\"text-align: right;\">     82097</td><td style=\"text-align: right;\">    184322</td><td style=\"text-align: right;\">     25695</td><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">   57564.1</td><td style=\"text-align: right;\">             0.0383668</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_case = test_yhat[test_yhat['ID'] == int(pred_percentile_dict[0])]\n",
    "test_case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test effect of changing `SEX`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SEX` should not have a large impact on predictions. This could indicate unwanted sociological bias in the GBM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">   ID</th><th style=\"text-align: right;\">  LIMIT_BAL</th><th>SEX  </th><th>EDUCATION  </th><th>MARRIAGE  </th><th style=\"text-align: right;\">  AGE</th><th>PAY_0         </th><th>PAY_2         </th><th>PAY_3         </th><th>PAY_4         </th><th>PAY_5         </th><th>PAY_6         </th><th style=\"text-align: right;\">  BILL_AMT1</th><th style=\"text-align: right;\">  BILL_AMT2</th><th style=\"text-align: right;\">  BILL_AMT3</th><th style=\"text-align: right;\">  BILL_AMT4</th><th style=\"text-align: right;\">  BILL_AMT5</th><th style=\"text-align: right;\">  BILL_AMT6</th><th style=\"text-align: right;\">  PAY_AMT1</th><th style=\"text-align: right;\">  PAY_AMT2</th><th style=\"text-align: right;\">  PAY_AMT3</th><th style=\"text-align: right;\">  PAY_AMT4</th><th style=\"text-align: right;\">  PAY_AMT5</th><th style=\"text-align: right;\">  PAY_AMT6</th><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  bill_std</th><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">       p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">28716</td><td style=\"text-align: right;\">     780000</td><td>male </td><td>university </td><td>single    </td><td style=\"text-align: right;\">   41</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td style=\"text-align: right;\">     101957</td><td style=\"text-align: right;\">      61715</td><td style=\"text-align: right;\">      38686</td><td style=\"text-align: right;\">      21482</td><td style=\"text-align: right;\">      72628</td><td style=\"text-align: right;\">     182792</td><td style=\"text-align: right;\">     62819</td><td style=\"text-align: right;\">     39558</td><td style=\"text-align: right;\">     22204</td><td style=\"text-align: right;\">     82097</td><td style=\"text-align: right;\">    184322</td><td style=\"text-align: right;\">     25695</td><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">   57564.1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.959052</td><td style=\"text-align: right;\">0.0409481</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_case = test_yhat[test_yhat['ID'] == int(pred_percentile_dict[0])]\n",
    "test_case = test_case.drop([yhat])\n",
    "test_case['SEX'] = 'male'\n",
    "test_case = test_case.cbind(model.predict(test_case))\n",
    "test_case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As desired, simulating this person as a male does not have a large impact on their probability of default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test effect of changing `PAY_0`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable importance and residual analysis indicates that the value of `PAY_0` can have a strong effect on model predictions. Measuring the change in predicted probability when the value of `PAY_0` is changed from a timely payment to late payment is probably a good test case for prediction stability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">   ID</th><th style=\"text-align: right;\">  LIMIT_BAL</th><th>SEX   </th><th>EDUCATION  </th><th>MARRIAGE  </th><th style=\"text-align: right;\">  AGE</th><th>PAY_0        </th><th>PAY_2         </th><th>PAY_3         </th><th>PAY_4         </th><th>PAY_5         </th><th>PAY_6         </th><th style=\"text-align: right;\">  BILL_AMT1</th><th style=\"text-align: right;\">  BILL_AMT2</th><th style=\"text-align: right;\">  BILL_AMT3</th><th style=\"text-align: right;\">  BILL_AMT4</th><th style=\"text-align: right;\">  BILL_AMT5</th><th style=\"text-align: right;\">  BILL_AMT6</th><th style=\"text-align: right;\">  PAY_AMT1</th><th style=\"text-align: right;\">  PAY_AMT2</th><th style=\"text-align: right;\">  PAY_AMT3</th><th style=\"text-align: right;\">  PAY_AMT4</th><th style=\"text-align: right;\">  PAY_AMT5</th><th style=\"text-align: right;\">  PAY_AMT6</th><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  bill_std</th><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">      p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">28716</td><td style=\"text-align: right;\">     780000</td><td>female</td><td>university </td><td>single    </td><td style=\"text-align: right;\">   41</td><td>2 month delay</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td>no consumption</td><td style=\"text-align: right;\">     101957</td><td style=\"text-align: right;\">      61715</td><td style=\"text-align: right;\">      38686</td><td style=\"text-align: right;\">      21482</td><td style=\"text-align: right;\">      72628</td><td style=\"text-align: right;\">     182792</td><td style=\"text-align: right;\">     62819</td><td style=\"text-align: right;\">     39558</td><td style=\"text-align: right;\">     22204</td><td style=\"text-align: right;\">     82097</td><td style=\"text-align: right;\">    184322</td><td style=\"text-align: right;\">     25695</td><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">   57564.1</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.571032</td><td style=\"text-align: right;\">0.428968</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_case = test_yhat[test_yhat['ID'] == int(pred_percentile_dict[0])]\n",
    "test_case = test_case.drop([yhat])\n",
    "test_case['PAY_0'] = '2 month delay' \n",
    "test_case = test_case.cbind(model.predict(test_case))\n",
    "test_case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the value is changed from `no consumption` to `two month delay` there is a very large increase in predicted probability. Such a marked change related to the value of one variable is problematic for numerous reasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use trained model to test predictions for interesting situations: customer most likely to default\n",
    "Now the same test will be performed on the customer most likely to default. This woman has a very high probability of default under the GBM model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">   ID</th><th style=\"text-align: right;\">  LIMIT_BAL</th><th>SEX   </th><th>EDUCATION  </th><th>MARRIAGE  </th><th style=\"text-align: right;\">  AGE</th><th>PAY_0        </th><th>PAY_2        </th><th>PAY_3        </th><th>PAY_4        </th><th>PAY_5        </th><th>PAY_6        </th><th style=\"text-align: right;\">  BILL_AMT1</th><th style=\"text-align: right;\">  BILL_AMT2</th><th style=\"text-align: right;\">  BILL_AMT3</th><th style=\"text-align: right;\">  BILL_AMT4</th><th style=\"text-align: right;\">  BILL_AMT5</th><th style=\"text-align: right;\">  BILL_AMT6</th><th style=\"text-align: right;\">  PAY_AMT1</th><th style=\"text-align: right;\">  PAY_AMT2</th><th style=\"text-align: right;\">  PAY_AMT3</th><th style=\"text-align: right;\">  PAY_AMT4</th><th style=\"text-align: right;\">  PAY_AMT5</th><th style=\"text-align: right;\">  PAY_AMT6</th><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  bill_std</th><th style=\"text-align: right;\">  p_DEFAULT_NEXT_MONTH</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">29116</td><td style=\"text-align: right;\">      20000</td><td>female</td><td>university </td><td>married   </td><td style=\"text-align: right;\">   59</td><td>3 month delay</td><td>2 month delay</td><td>3 month delay</td><td>2 month delay</td><td>2 month delay</td><td>4 month delay</td><td style=\"text-align: right;\">       8803</td><td style=\"text-align: right;\">      11137</td><td style=\"text-align: right;\">      10672</td><td style=\"text-align: right;\">      11201</td><td style=\"text-align: right;\">      12721</td><td style=\"text-align: right;\">      11946</td><td style=\"text-align: right;\">      2800</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">      1000</td><td style=\"text-align: right;\">      2000</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">   1327.55</td><td style=\"text-align: right;\">              0.895285</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_case = test_yhat[test_yhat['ID'] == int(pred_percentile_dict[99])]\n",
    "test_case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test effect of changing `SEX`\n",
    "Changing the value for `SEX` from female to male for this customer decreases the predicted probability by a relatively small amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">   ID</th><th style=\"text-align: right;\">  LIMIT_BAL</th><th>SEX  </th><th>EDUCATION  </th><th>MARRIAGE  </th><th style=\"text-align: right;\">  AGE</th><th>PAY_0        </th><th>PAY_2        </th><th>PAY_3        </th><th>PAY_4        </th><th>PAY_5        </th><th>PAY_6        </th><th style=\"text-align: right;\">  BILL_AMT1</th><th style=\"text-align: right;\">  BILL_AMT2</th><th style=\"text-align: right;\">  BILL_AMT3</th><th style=\"text-align: right;\">  BILL_AMT4</th><th style=\"text-align: right;\">  BILL_AMT5</th><th style=\"text-align: right;\">  BILL_AMT6</th><th style=\"text-align: right;\">  PAY_AMT1</th><th style=\"text-align: right;\">  PAY_AMT2</th><th style=\"text-align: right;\">  PAY_AMT3</th><th style=\"text-align: right;\">  PAY_AMT4</th><th style=\"text-align: right;\">  PAY_AMT5</th><th style=\"text-align: right;\">  PAY_AMT6</th><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  bill_std</th><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">      p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">29116</td><td style=\"text-align: right;\">      20000</td><td>male </td><td>university </td><td>married   </td><td style=\"text-align: right;\">   59</td><td>3 month delay</td><td>2 month delay</td><td>3 month delay</td><td>2 month delay</td><td>2 month delay</td><td>4 month delay</td><td style=\"text-align: right;\">       8803</td><td style=\"text-align: right;\">      11137</td><td style=\"text-align: right;\">      10672</td><td style=\"text-align: right;\">      11201</td><td style=\"text-align: right;\">      12721</td><td style=\"text-align: right;\">      11946</td><td style=\"text-align: right;\">      2800</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">      1000</td><td style=\"text-align: right;\">      2000</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">   1327.55</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.161579</td><td style=\"text-align: right;\">0.838421</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_case = test_yhat[test_yhat['ID'] == int(pred_percentile_dict[99])]\n",
    "test_case = test_case.drop([yhat])\n",
    "test_case['SEX'] = 'male'\n",
    "test_case = test_case.cbind(model.predict(test_case))\n",
    "test_case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test effect of changing `PAY_0`\n",
    "Switching the riskiest customer's value for `PAY_0` from `3 month delay` to `pay duly` reduces the their chance of default by about 20%, a noticable swing in probability but still a higher probability value, notably greater than common lending cutoffs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">   ID</th><th style=\"text-align: right;\">  LIMIT_BAL</th><th>SEX   </th><th>EDUCATION  </th><th>MARRIAGE  </th><th style=\"text-align: right;\">  AGE</th><th>PAY_0   </th><th>PAY_2        </th><th>PAY_3        </th><th>PAY_4        </th><th>PAY_5        </th><th>PAY_6        </th><th style=\"text-align: right;\">  BILL_AMT1</th><th style=\"text-align: right;\">  BILL_AMT2</th><th style=\"text-align: right;\">  BILL_AMT3</th><th style=\"text-align: right;\">  BILL_AMT4</th><th style=\"text-align: right;\">  BILL_AMT5</th><th style=\"text-align: right;\">  BILL_AMT6</th><th style=\"text-align: right;\">  PAY_AMT1</th><th style=\"text-align: right;\">  PAY_AMT2</th><th style=\"text-align: right;\">  PAY_AMT3</th><th style=\"text-align: right;\">  PAY_AMT4</th><th style=\"text-align: right;\">  PAY_AMT5</th><th style=\"text-align: right;\">  PAY_AMT6</th><th style=\"text-align: right;\">  DEFAULT_NEXT_MONTH</th><th style=\"text-align: right;\">  bill_std</th><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">      p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">29116</td><td style=\"text-align: right;\">      20000</td><td>female</td><td>university </td><td>married   </td><td style=\"text-align: right;\">   59</td><td>pay duly</td><td>2 month delay</td><td>3 month delay</td><td>2 month delay</td><td>2 month delay</td><td>4 month delay</td><td style=\"text-align: right;\">       8803</td><td style=\"text-align: right;\">      11137</td><td style=\"text-align: right;\">      10672</td><td style=\"text-align: right;\">      11201</td><td style=\"text-align: right;\">      12721</td><td style=\"text-align: right;\">      11946</td><td style=\"text-align: right;\">      2800</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">      1000</td><td style=\"text-align: right;\">      2000</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">   1327.55</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.273858</td><td style=\"text-align: right;\">0.726142</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_case = test_yhat[test_yhat['ID'] == int(pred_percentile_dict[99])]\n",
    "test_case = test_case.drop([yhat])\n",
    "test_case['PAY_0'] = 'pay duly' \n",
    "test_case = test_case.cbind(model.predict(test_case))\n",
    "test_case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this small number of boundary test cases, the GBM model appears stable. However, if large swings in predictions occur for sensitive or important variables, practicioners are urged to retrain unstable models without the problematic variables or combinations of variables, which may unfortunately involve some trial and error. Also, four test cases is woefully inadequate for real-world models. Automated sensitivity analysis across many variables, combinations of variables, and for many different rows of data seems more appropriate for mission-critical machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shutdown H2O\n",
    "After using h2o, it's typically best to shut it down. However, before doing so, users should ensure that they have saved any h2o data structures, such as models, H2OFrames, or scoring artifacts, such as POJOs or MOJOs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? n\n"
     ]
    }
   ],
   "source": [
    "# be careful, this can erase your work!\n",
    "h2o.cluster().shutdown(prompt=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, a complex GBM classifier was trained to predict credit card defaults. Residual analysis was used to debug the GBM model predictions and enabled a slight improvement in accuracy. Sensitivity analysis was used to test the GBM for trustworthiness and stability. In a small number of boundary test cases, the trained GBM appeared stable. Residual analysis and sensitivity analysis are powerful model debugging techniques and can increase trust in complex models. These techniques should generalize well for many types of business and research problems, enabling you to train a complex model and justify it to your colleagues, bosses, and potentially, external regulators. "
   ]
  }
 ],
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